ABSTRACT
In turn, inverse problems can also be classified as well-posed or ill-posed. A well-posed inverse problem is one in which a solution exists, is unique,
and depends univocally and continuously on the data; otherwise, it is said to be an ill-posed inverse problem. Experience shows that most inverse problems are ill posed. Usually, one needs to add prior information about the functional set of the expected solution to obtain a stable estimate of the solution; this is called regularization of an ill-posed inverse problem.
